Create an R Kernel for a Jupyter Notebook

Owned by Simon Alexander
Last updated: Aug 26, 2024
2 min read

Goal: Detail how to update an R Conda environment so it can be used as a kernel within ARCC’s Jupyter service.

What is Jupyter and What is a Kernel?

  • Jupyter is a large umbrella project that covers many different software offerings and tools, including the popular Jupyter Notebook and JupyterLab web-based notebook authoring and editing applications.

  • Kernels are programming language specific processes that run independently and interact with the Jupyter Applications and their user interfaces.

    • They can be considered as the computational engine for notebooks.

    • The kernel process is a standalone process that runs in the background and executes the code that you write in your notebooks. The kernel process is responsible for running the code and returning the results to the frontend.

  • ARCC provides the Jupyter with OnDemand workshop.

General Process

The general process involves updating the Conda environment to include kernel related packages, and then configuring the kernel spec to allow it to be picked up by the Jupyter service.

  1. Activate you R Conda Environment.

  2. Start R.

  3. Install the IRkernel package.

  4. Exit R.

  5. Deactivate your Conda environment.

  6. Copy the created kernelspec into your home ~/.local/share/jupyter/kernels/ folder.

  7. Update the kernel.json.

Install the IRkernel package

[]$ module load miniconda3/24.3.0 []$ conda activate /project/<project-name>/software/conda-envs/r_4.3.3_env/ (/project/<project-name>/software/conda-envs/r_4.3.3_env) []$ R > install.packages('IRkernel') ... also installing the dependencies ‘fastmap’, ‘fansi’, ‘utf8’, ‘htmltools’, ‘pillar’, ‘base64enc’, ‘repr’, ‘evaluate’, ‘IRdisplay’, ‘pbdZMQ’, ‘crayon’, ‘jsonlite’, ‘uuid’, ‘digest’ ... * DONE (IRkernel)

Created kernelspec folder

[]$ ls /project/<project-name>/software/conda-envs/r_4.3.3_env/lib/R/library/IRkernel/kernelspec/ kernel.js kernel.json logo-64x64.png logo-svg.svg
[salexan5@mblog2 ~]$ cat /project/<project-name>/software/conda-envs/r_4.3.3_env/lib/R/library/IRkernel/kernelspec/kernel.json {"argv": ["R", "--slave", "-e", "IRkernel::main()", "--args", "{connection_file}"], "display_name":"R", "language":"R" }

Configure Your Jupyter Environment

Start Jupyter

image-20240826-184721.png
Notice the new R_4.3.3 kernel.

Within a Notebook

Within a cell try:

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